ABSTRACT Introduction: In recent years, there has been a progressive and significant increase in healthcare demand from citizens, including in terms of Waiting Lists. This growth has been triggered by various factors: demographic expansion, technological advancements in the surgical field, the ageing phenomenon, and, starting from 2020, the SARS-CoV-2 pandemic, which has created additional challenges by slowing down the operational capacity of Operating Theatres. This study is part of a project launched by the University Hospital of the Marche Region and promoted by the Italian Ministry of Health, in line with the objectives explicitly defined in the current National Waiting Lists Management Plan (PNGLA). The main aim is to improve the monitoring and management processes of elective surgical admissions. Objective: The objective was to validate a minimum dataset to be collected digitally at the time of the patient’s inclusion on the list, in order to assess its adequacy for calculating performance indicators. Materials and Methods: A prospective observational study was conducted on a sample of 506 adult patients placed on the Waiting List at the University Hospital of the Marche Region. Data collection took place from April 2, 2024, to September 27, 2024, through two company information systems, and the data were analysed using descriptive tools. All participants provided informed consent, and the study was approved by the Territorial Ethics Committee. The minimum dataset included demographic, clinical, organisational, and time-related information necessary for calculating nationally established performance indicators, such as: list size, waiting times, threshold exceedance, use of pre-hospitalisation, and active/passive mobility. Results: The analysis revealed a progressive reduction in the list size during the summer months; however, a high percentage of patients experienced waiting times exceeding the set thresholds, particularly in priority classes A and B. Specifically, 48% of Class A admissions exceeded the 30-day limit, while in Class B, the percentage was 38%. The median waiting times showed significant variations among different disciplines and geographical areas of origin. Only 16.8% of patients underwent pre-hospitalisation, and 13% of surgeries were performed under active mobility. A detailed analysis of the Obstetrics and Gynaecology Department confirmed the overall critical issues, with particularly long waiting times in Class C (up to 1,428 days). Discussion: The minimum dataset proved to be useful and effective for both data collection and patient flow analysis. However, the study highlighted significant critical issues, including heterogeneity in waiting times and a number of admissions insufficient to fully meet healthcare demand. The results confirm the need to adopt a continuous and dynamic monitoring system capable of identifying patients at risk of exceeding thresholds and optimising the use of available resources. Conclusion: In conclusion, the digitalised minimum dataset has proven valid for the governance of Waiting Lists. However, the need for targeted improvement actions emerges, aimed at a unified, centralised, and comprehensive management of the phenomenon. In this context, the improvement plan initiated by the University Hospital of the Marche Region is fundamental, promoting a dynamic approach based on the continuous improvement cycle (Plan-Do-Check-Act).
ABSTRACT Introduzione: Negli ultimi anni si è assistito ad una progressiva e sempre maggiore crescita di domanda di salute da parte dei cittadini, anche in termini di Liste di Attesa. Tale crescita è stata innescata da diverse cause: l’aumento del fattore demografico, l’evoluzione tecnologica in ambito chirurgico, l’aumento del fenomeno dell’invecchiamento e, a partire dal 2020, anche il SARS-CoV-2 che ha creato ulteriori criticità andando a rallentare l’operatività delle Sale Operatorie. Il presente studio si inserisce all’interno di un progetto avviato dall’Azienda Ospedaliero Universitaria delle Marche e promosso dal Ministero della Salute, in linea con gli obiettivi esplicitamente definiti dal PNGLA vigente. Lo scopo principale è il miglioramento dei processi di monitoraggio e gestione dei ricoveri chirurgici programmati. Obiettivo: L'obiettivo è stato quello di validare un minimum dataset da raccogliere in modalità informatizzata al momento dell'inserimento del paziente in lista, al fine di verificarne l’adeguatezza per il calcolo degli indicatori di performance. Materiali e Metodi: È stato condotto uno studio osservazionale prospettico su un campione di 506 pazienti adulti inseriti in Lista di Attesa presso l’AOU delle Marche. La raccolta dati è avvenuta dal 2 aprile 2024 al 27 settembre 2024 tramite due applicativi aziendali, e analizzati con strumenti descrittivi. Tutti i partecipanti hanno fornito il consenso informato e lo studio ha ottenuto l’approvazione del Comitato Etico Territoriale. Il minimum data set comprendeva informazioni anagrafiche, cliniche, organizzative e temporali, necessarie per il calcolo degli indicatori di performance stabiliti nelle linee guida nazionali, tra cui: consistenza della lista, tempi di attesa, superamento delle soglie, utilizzo del pre-ricovero e mobilità attiva/passiva. Risultati: L’analisi ha evidenziato una riduzione progressiva della consistenza della lista nei mesi estivi, accompagnata tuttavia da un’elevata percentuale di pazienti con tempi di attesa superiori ai limiti previsti, in particolare nelle classi A e B. Il 48% dei ricoveri in Classe A è stato eseguito oltre la soglia di 30 giorni, mentre nella Classe B la percentuale è stata del 38%. La mediana dei tempi di attesa ha mostrato ampie variazioni tra le discipline e i territori di provenienza. Solo il 16,8% dei pazienti ha usufruito del pre-ricovero, e 13% degli interventi è stato eseguito per mobilità attiva. L’approfondimento condotto sulla Clinica di Ostetricia e Ginecologia ha confermato le criticità generali, con tempi di attesa elevati, in particolare nella Classe C (fino a 1428 giorni). Discussione: Il minimum data set si è dimostrato utile ed efficace sia per la raccolta di informazioni sia per l’analisi dei flussi dei pazienti. Tuttavia, l’indagine ha evidenziato criticità significative: disomogeneità nei tempi di attesa e un numero di ricoveri non in grado di soddisfare a pieno la domanda di salute. I risultati confermano la necessità di adottare un sistema di monitoraggio continuo e dinamico, in grado di identificare i pazienti a rischio di superamento soglia e di ottimizzare l’uso delle risorse disponibili. Conclusione: In conclusione, si è confermata la validità del minimum data set informatizzo per il governo delle Liste d’Attesa. Tuttavia, emerge l’urgenza di interventi migliorativi mirati volti a una gestione unica, centralizzata e completa del fenomeno. Fondamentale, quindi il piano di miglioramento avviato dall’AOU delle Marche finalizzato ad un approccio dinamico basato sul ciclo di miglioramento continuo (Plan-Do-Check-Act).
La gestione dei flussi informativi delle Liste d'Attesa per i ricoveri chirurgici programmati
SORICETTI, ELEONORA
2024/2025
Abstract
ABSTRACT Introduction: In recent years, there has been a progressive and significant increase in healthcare demand from citizens, including in terms of Waiting Lists. This growth has been triggered by various factors: demographic expansion, technological advancements in the surgical field, the ageing phenomenon, and, starting from 2020, the SARS-CoV-2 pandemic, which has created additional challenges by slowing down the operational capacity of Operating Theatres. This study is part of a project launched by the University Hospital of the Marche Region and promoted by the Italian Ministry of Health, in line with the objectives explicitly defined in the current National Waiting Lists Management Plan (PNGLA). The main aim is to improve the monitoring and management processes of elective surgical admissions. Objective: The objective was to validate a minimum dataset to be collected digitally at the time of the patient’s inclusion on the list, in order to assess its adequacy for calculating performance indicators. Materials and Methods: A prospective observational study was conducted on a sample of 506 adult patients placed on the Waiting List at the University Hospital of the Marche Region. Data collection took place from April 2, 2024, to September 27, 2024, through two company information systems, and the data were analysed using descriptive tools. All participants provided informed consent, and the study was approved by the Territorial Ethics Committee. The minimum dataset included demographic, clinical, organisational, and time-related information necessary for calculating nationally established performance indicators, such as: list size, waiting times, threshold exceedance, use of pre-hospitalisation, and active/passive mobility. Results: The analysis revealed a progressive reduction in the list size during the summer months; however, a high percentage of patients experienced waiting times exceeding the set thresholds, particularly in priority classes A and B. Specifically, 48% of Class A admissions exceeded the 30-day limit, while in Class B, the percentage was 38%. The median waiting times showed significant variations among different disciplines and geographical areas of origin. Only 16.8% of patients underwent pre-hospitalisation, and 13% of surgeries were performed under active mobility. A detailed analysis of the Obstetrics and Gynaecology Department confirmed the overall critical issues, with particularly long waiting times in Class C (up to 1,428 days). Discussion: The minimum dataset proved to be useful and effective for both data collection and patient flow analysis. However, the study highlighted significant critical issues, including heterogeneity in waiting times and a number of admissions insufficient to fully meet healthcare demand. The results confirm the need to adopt a continuous and dynamic monitoring system capable of identifying patients at risk of exceeding thresholds and optimising the use of available resources. Conclusion: In conclusion, the digitalised minimum dataset has proven valid for the governance of Waiting Lists. However, the need for targeted improvement actions emerges, aimed at a unified, centralised, and comprehensive management of the phenomenon. In this context, the improvement plan initiated by the University Hospital of the Marche Region is fundamental, promoting a dynamic approach based on the continuous improvement cycle (Plan-Do-Check-Act).| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12075/22290